Comparison of simulations from a state-of-the-art dynamic global vegetation model (LPJ-GUESS) driven by low- and high-resolution climate data

Abstract

Simulations of dynamic global vegetation models (DGVMs) are typically conducted at a spatial resolution of 0.5°, while higher-resolution simulations remain uncommon. This coarse resolution eliminates detailed orographic features and hence, associated climate variability, which are especially pronounced in mountainous regions. The impact of disregarding such variability on vegetation dynamics has not been thoroughly examined. In this study, we explore the differences in vegetation outcomes between the DGVM LPJ-GUESS simulations conducted at high and low spatial resolutions. Using the CHELSA algorithm, we create an elevation-informed high-resolution climate dataset for a domain encompassing the European Union. Distinctive features of this algorithm include orographic nature of formation of precipitation, a negative derivative of temperatures with respect to elevation, and also, detailed consideration of shadowing and exposure of the terrain to the Sun in computations of solar radiation. We design a custom experiment protocol and use it to perform LPJ-GUESS simulations on both resolutions. Comparative analysis reveals significant systematic discrepancies between the two resolutions. In mountainous areas, all of the considered output variables show statistically significant differences. In particular, carbon pools are smaller on the high resolution, with the total carbon pool being 37 %-39 % smaller. Furthermore, we quantify the extent to which the under-representation of orographic climate variation affects regional predictions across the European Union. This is expressed as a difference in the total value, which ranges from -3.8% for the net ecosystem productivity to 2.9 % for the litter and soil C pools. These values are found to be comparable to differences caused by miss-representation of water bodies and shorelines on the low resolution.